The proposed method has an end-to-end encoder-decoder structure, illustrated in Figure REF .
The approach is a lightweight structure that exploits the powerful capabilities of information encoding-decoding, and therefore, it is able to handle full-resolution (FR) inputs efficiently.
The UNet-like structure consists of many sampling blocks and residual groups to obtain deep features. The main components are the “residual group" [1]} and the “enhanced block" [2]}, these can be visualized Figure REF .
Authors emphasize that since many modules in the ISP are based on full-resolution (e.g.  global tone mapping, auto white balance, and lens shading correction), FR training and testing improves performance in comparison to patch-based approaches.
The method was implemented in PyTorch 1.8, and was trained roughly 13 hrs on 4\(\times \)  V100 GPU using FR images, L1 loss and SSIM loss, and batch size 1.
Authors correct misalignments and black leve of RAW images.
We must highlight that the method is able to process 4K images in 18ms in a single V100 GPU. Moreover, it generalizes successfully to noisy inputs and other unseen sensors (see Table REF ). The code will be open-sourced.
